It is a long-standing challenge in modern recommender systems to effectively make recommendations for new users, namely the cold-start problem. Cross-Domain Recommendation (CDR) has been proposed to address this challenge, but current ways to represent users' interests across systems are still severely limited. We introduce Personal Knowledge Graph (PKG) as a domain-invariant interest representation, and propose a novel CDR paradigm named MeKB-Rec. We first link users and entities in a knowledge base to construct a PKG of users' interests, named MeKB. Then we learn a semantic representation of MeKB for the cross-domain recommendation. To efficiently utilize limited training data in CDR, MeKB-Rec employs Pretrained Language Models to inject world knowledge into understanding users' interests. Beyond most existing systems, our approach builds a semantic mapping across domains which breaks the requirement for in-domain user behaviors, enabling zero-shot recommendations for new users in a low-resource domain. We experiment MeKB-Rec on well-established public CDR datasets, and demonstrate that the new formulation % is more powerful than previous approaches, achieves a new state-of-the-art that significantly improves HR@10 and NDCG@10 metrics over best previous approaches by 24\%--91\%, with a 105\% improvement for HR@10 of zero-shot users with no behavior in the target domain. We deploy MeKB-Rec in WeiXin recommendation scenarios and achieve significant gains in core online metrics. MeKB-Rec is now serving hundreds of millions of users in real-world products.

翻译：现代推荐系统长期面临为新用户有效生成推荐这一挑战，即冷启动问题。跨领域推荐（CDR）被提出以应对这一挑战，但现有跨系统用户兴趣表示方法仍存在严重局限。我们提出将个人知识图谱（PKG）作为领域无关的兴趣表示，并设计了一种新型CDR范式MeKB-Rec。首先，我们将用户与知识库中的实体进行链接，构建用户兴趣的个人知识图谱（命名为MeKB）；随后，学习MeKB的语义表示以支撑跨领域推荐。为高效利用CDR中有限的训练数据，MeKB-Rec采用预训练语言模型注入世界知识以理解用户兴趣。与现有大多数系统不同，本方法构建了跨领域的语义映射，打破了对领域内用户行为的依赖，从而能在低资源领域中对新用户实现零样本推荐。在权威公开CDR数据集上的实验表明，该新范式优于以往方法，达到了新的最优水平：核心指标HR@10和NDCG@10相较最佳基线方法显著提升24%至91%，其中针对目标领域无行为的零样本用户，HR@10提升达105%。我们在微信推荐场景中部署MeKB-Rec，并取得了核心在线指标的显著增益。目前，MeKB-Rec已在现实产品中服务于数亿用户。